Methods for addressing climate change uncertainties in project environmental impact assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change has important implications for assessing impacts of many types of project. If climate change is to be included in environmental assessments, then proponents must be able to incorporate its impacts and inherent uncertainties effectively into their analysis; many proponents do not possess sufficient grounding in how to accomplish this task successfully. In this paper, three basic analytical approaches to uncertainty analysis — scenario analysis, sensitivity analysis, and probabilistic analysis — are presented that proponents could use for integrating climate change induced impacts and their uncertainties into their environmental assessments, together with a framework for judging the circumstances that determine which method would be applicable. The use of these three approaches is illustrated on the environmental impacts of a run-of-the-river hydroelectric project.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it